# Estimating win probability from best-of-7 series is not straightforward

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*(Note: The code in this post is available here as a single R script.)*

Let’s say A and B play 7 games and A wins 4 of them. * What would be a reasonable estimate for A’s win probability (over B)? *A natural estimate would be 4/7. More generally, if A wins games, then the estimator is an unbiased estimator of the true win probability . This is not difficult to show: since , we have

Now, let’s say that A and B play a *best-of-7 series** (or equivalently, first to 4 wins)*, and A wins 4 of them. It seems natural to estimate A’s win probability by 4/7 again. If we define the estimator

* does this estimator have any nice properties? *It’s hard to say anything about this estimator (if someone knows something, please let me know!). In fact, it’s not even unbiased!

**Estimator is biased: a simulation**

First, let’s set up two functions. The first function, `SampleSeries`

, simulates a series which ends when either player 1 reaches `n_wins1`

wins or when player 2 reaches `n_wins2`

wins. (The true win probability for player 1 is `p`

.) The second function, `EstimateWinProb`

, samples `n_sim`

series. For each series, it estimates player 1’s win probability as in above, then returns the mean of over the `n_sim`

simulations.

# Player 1 has win probability p. Simulates a series of games which ends when # player 1 reaches `n_wins1` wins or when player 2 reaches `n_wins2` wins. SampleSeries <- function(p, n_wins1, n_wins2 = n_wins1) { game_results <- rbinom(n_wins1 + n_wins2 - 1, size = 1, prob = p) if (sum(game_results) >= n_wins1) { n_games <- which(game_results == 1)[n_wins1] } else { n_games <- which(game_results == 0)[n_wins2] } player1_wins <- sum(game_results[1:n_games]) return(c(player1_wins, n_games - player1_wins)) } # Run SampleSeries `n_sim` times and for each run, estimate p by player 1's # win percentage in that series. Returns the estimated probabilities. EstimateWinProb <- function(p, n_sim, n_wins1, n_wins2 = n_wins1) { win_data <- replicate(n_sim, SampleSeries(p, n_wins1, n_wins2)) prob_estimates <- apply(win_data, 2, function(x) x[1] / (x[1] + x[2])) return(mean(prob_estimates)) }

Next, we run a Monte Carlo simulation (20,000 simulations) to compute the mean of for a range of values of . (Because of the symmetry inherent in the setup, we only consider .)

set.seed(1) p <- 20:40 / 40 n_wins1 <- 4 n_sim <- 20000 mean_phat <- sapply(p, function(p) EstimateWinProb(p, n_sim, n_wins1)) plot(p, mean_phat, asp = 1, pch = 16, ylab = "Win prob estimator mean", main = "First to 4 wins") abline(0, 1)

If were unbiased, we would expect the black dots to lie on the black line ; it’s clear that they don’t. In this setting, is biased upwards.

**Estimator is biased: exact computation**

I was not able to find a nice closed-form expression for : for a best-of-7 series, WolframAlpha “simplified” my expression to give

However, it is possible to write a function that computes from first principles:

# Return expected value of the "series win percentage" estimator GetEstimatorMean <- function(p, n_wins1, n_wins2 = n_wins1) { # first line is for when player 1 wins, second line is for when player 2 wins summands1 <- sapply(0:(n_wins2 - 1), function(x) n_wins1 / (n_wins1 + x) * p^n_wins1 * (1 - p)^x * choose(x + n_wins1 - 1, x) ) summands2 <- sapply(0:(n_wins1 - 1), function(x) x / (x + n_wins2) * p^x * (1 - p)^n_wins2 * choose(x + n_wins2 - 1, x) ) return(sum(c(summands1, summands2))) }

Let’s plot these exact values on the previous plot to make sure we got it right:

lines(p, sapply(p, function(x) GetEstimatorMean(x, n_wins1)), col = "red", lty = 2, lwd = 1.5)

**What might be a better estimator?**

Let’s go back to the setup where A and B play a *best-of-7 series** (or equivalently, first to 4 wins)*, and A wins 4 of them. Instead of estimating A’s win probability by 4/7, we could use the plot above to find which value of would have given , and use that instead. This is essentially a * method of moments estimator* with a single data point, and so has some nice properties (well, at least it’s consistent).

The picture below depicts this method for obtaining estimates for when A wins 4 out of 4, 5, 6 and 7 games.

p <- 20:40 / 40 original_estimate <- c(4 / 7:4) mm_estimate <- c(0.56, 0.64, 0.77, 1) # by trial and error plot(p, sapply(p, function(x) GetEstimatorMean(x, n_wins1 = 4)), type = "l", asp = 1, ylab = "Win prob estimator mean") abline(0, 1, lty = 2) segments(x0 = 0, x1 = mm_estimate, y0 = original_estimate, col = "red", lty = 2) segments(y0 = 0, y1 = original_estimate, x0 = mm_estimate, col = "red", lty = 2)

**What are some other possible estimators in this setting?**

**First to n wins: generalizing the above**

Since we’ve coded up all this already, why look at just first to 4 wins? Let’s see what happens to the estimator when we vary the number of wins the players need in order to win the series. The code below makes a plot showing how varies as we change the number of wins needed from 1 to 8 (inclusive).

library(tidyverse) theme_set(theme_bw()) df <- expand.grid(p = 20:40 / 40, n_wins1 = 1:8) df$mean_phat <- apply(df, 1, function(x) GetEstimatorMean(x[1], x[2])) ggplot(df, aes(group = factor(n_wins1))) + geom_line(aes(x = p, y = mean_phat, col = factor(n_wins1))) + coord_fixed(ratio = 1) + scale_color_discrete(name = "n_wins") + labs(y = "Win prob estimator mean", title = "First to n_wins")

Past some point, the bias decreases as `n_wins1`

increases. Peak bias seems to be around `n_wins1 = 3`

. The picture below zooms in on a small portion of the picture above:

**Asymmetric series: different win criteria for the players**

Notice that in the code above, we maintained separate parameters for player 1 and player 2 (`n_wins1`

and `n_wins2`

). This allows us to generalize even further, to explore what happens when players 1 and 2 need a different number of wins in order to win the series.

The code below shows how varies with for different combinations of `n_wins1`

and `n_wins2`

. Each line within a facet corresponds to one value of `n_wins1`

, while each facet of the plot corresponds to one value of `n_wins2`

. The size of ‘s bias increases as the difference between `n_wins1`

and `n_wins2`

increases.

df <- expand.grid(p = 20:40 / 40, n_wins1 = 1:8, n_wins2 = 1:8) df$mean_phat <- apply(df, 1, function(x) GetEstimatorMean(x[1], x[2], x[3])) ggplot(df, aes(group = factor(n_wins1))) + geom_line(aes(x = p, y = mean_phat, col = factor(n_wins1))) + geom_abline(slope = 1, intercept = 0, linetype = "dashed") + coord_fixed(ratio = 1) + scale_color_discrete(name = "n_wins1") + facet_wrap(~ n_wins2, ncol = 4, labeller = label_both) + labs(y = "Win prob estimator mean", title = "Player 1 to n_wins1 or Player 2 to n_wins2")

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